A Review of Stochastic Programming Methods for Optimization of Process Systems Under Uncertainty

Uncertainties are widespread in the optimization of process systems, such as uncertainties technologies, prices, and customer demands. In this paper, we review basic concepts recent advances a risk-neutral mathematical framework called “stochastic programming” its applications solving systems engineering problems under uncertainty. This intends to provide both tutorial for beginners without prior experience high-level overview current state-of-the-art developments experts stochastic programming. The formulations algorithms two-stage multistage programming reviewed with illustrative examples from industries. differences between exogenous uncertainty endogenous discussed. several data-driven methods generating scenario trees also reviewed.